Mark
00c7614bfc
Fix critical typos in class names and variables that could cause confusion
and runtime errors.
Class name fixes:
- trandeVoter → TradeVoter (market_trade/core/trandeVoter.py)
- decsionManager → DecisionManager (market_trade/core/decisionManager_v2.py)
- coreSignalTrande → CoreSignalTrade (market_trade/core/signals_v2.py)
- coreIndicator → CoreIndicator (market_trade/core/indicators_v2.py)
- indicatorsAgrigator → IndicatorsAggregator (indicators_v2.py)
- signalsAgrigator → SignalsAggregator (signals_v2.py)
- riskManager → RiskManager (market_trade/core/riskManager.py)
Variable typo fixes:
- commision → commission (riskManager.py, lines 8-9, 24)
- probabilityDecsion → probability_decision (decisionManager_v2.py:84)
Type hint corrections:
- Fixed pd.DataFrame() → pd.DataFrame (incorrect syntax in 4 files)
Bug fixes:
- Fixed mutable default argument antipattern in indicators_v2.py:33
(indDict={} → indDict=None)
- Fixed mutable default argument in CoreTradeMath.py:22
(params={} → params=None)
All class references updated throughout the codebase to maintain
consistency.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
75 lines
3.6 KiB
Python
75 lines
3.6 KiB
Python
import pandas as pd
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import datetime
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import numpy as np
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#import random
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class TradeVoter():
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def __init__(self, name):
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self.name = name # Instance identifier
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self.trade_values_list = ['up', 'none', 'down'] # Valid trade directions
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self.matrix_amounts = None # Sum matrix for signal combinations
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self.keys_matrix_amounts = None # Matrix keys, technical field
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self.matrix_probability = None # Probability matrix for decision making
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# Function to create DataFrame with specified columns and indices. Indices are unique combinations.
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def create_df_by_names(self, names_index, column_names, default_value=0.0):
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df = pd.DataFrame(dict.fromkeys(column_names, [default_value]*pow(3, len(names_index))),
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index=pd.MultiIndex.from_product([self.trade_values_list]*len(names_index), names=names_index)
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)
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return df
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# Create sum matrix with default value
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def create_matrix_amounts(self, names_index: list) -> pd.DataFrame:
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self.matrix_amounts = self.create_df_by_names(names_index, self.trade_values_list, 0)
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self.keys_matrix_amounts = self.matrix_amounts.to_dict('tight')['index_names']
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self.create_matrix_probability(names_index)
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return self.matrix_amounts
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# Create probability matrix with default value
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def create_matrix_probability(self, names_index: list) -> pd.DataFrame:
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self.matrix_probability = self.create_df_by_names(names_index, self.trade_values_list)
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return self.matrix_probability
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# Set values in sum matrix. signalDecisions - indicator values key:value; trande - actual value
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def set_decision_by_signals(self, signal_decisions: dict, trande: str) -> None:
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buff = []
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for i in self.keys_matrix_amounts:
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buff.append(signal_decisions[i])
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self.matrix_amounts.loc[tuple(buff), trande] += 1
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# Fill probability matrix with calculated values from sum matrix
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def generate_matrix_probability(self) -> None:
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for i in range(self.matrix_amounts.shape[0]):
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print(self.matrix_amounts)
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row_sum = sum(self.matrix_amounts.iloc[i]) + 1
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self.matrix_probability.iloc[i]['up'] = self.matrix_amounts.iloc[i]['up'] / row_sum
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self.matrix_probability.iloc[i]['none'] = self.matrix_amounts.iloc[i]['none'] / row_sum
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self.matrix_probability.iloc[i]['down'] = self.matrix_amounts.iloc[i]['down'] / row_sum
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# Get decision from probability matrix based on signal values
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def get_decision_by_signals(self, signal_decisions: dict) -> dict:
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ans = {}
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splice_search = self.matrix_probability.xs(tuple(signal_decisions.values()),
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level=list(signal_decisions.keys())
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)
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ans['probability'] = splice_search.to_dict('records')[0]
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ans['trande'] = splice_search.iloc[0].idxmax()
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return ans
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# Get probability and sum matrices as dictionaries
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def get_matrix_dict(self) -> dict:
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ans = {}
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ans['amounts'] = self.matrix_amounts.to_dict('tight')
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ans['probability'] = self.matrix_probability.to_dict('tight')
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return ans
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# Set probability and sum matrices from dictionaries
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def set_matrix_dict(self, matrix_dict: dict) -> dict:
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if matrix_dict['amounts'] != None:
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self.matrix_amounts = pd.DataFrame.from_dict(y['amounts'], orient='tight')
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if matrix_dict['probability'] != None:
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self.matrix_probability = pd.DataFrame.from_dict(y['probability'], orient='tight')
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